mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +06:00
d8049331dc
* Init FNet * Update config * Fix config * Update model classes * Update tokenizers to use sentencepiece * Fix errors in model * Fix defaults in config * Remove position embedding type completely * Fix typo and take only real numbers * Fix type vocab size in configuration * Add projection layer to embeddings * Fix position ids bug in embeddings * Add minor changes * Add conversion script and remove CausalLM vestiges * Fix conversion script * Fix conversion script * Remove CausalLM Test * Update checkpoint names to dummy checkpoints * Add tokenizer mapping * Fix modeling file and corresponding tests * Add tokenization test file * Add PreTraining model test * Make style and quality * Make tokenization base tests work * Update docs * Add FastTokenizer tests * Fix fast tokenizer special tokens * Fix style and quality * Remove load_tf_weights vestiges * Add FNet to main README * Fix configuration example indentation * Comment tokenization slow test * Fix style * Add changes from review * Fix style * Remove bos and eos tokens from tokenizers * Add tokenizer slow test, TPU transforms, NSP * Add scipy check * Add scipy availabilty check to test * Fix tokenizer and use correct inputs * Remove remaining TODOs * Fix tests * Fix tests * Comment Fourier Test * Uncomment Fourier Test * Change to google checkpoint * Add changes from review * Fix activation function * Fix model integration test * Add more integration tests * Add comparison steps to MLM integration test * Fix style * Add masked tokenization fix * Improve mask tokenization fix * Fix index docs * Add changes from review * Fix issue * Fix failing import in test * some more fixes * correct fast tokenizer * finalize * make style * Remove additional tokenization logic * Set do_lower_case to False * Allow keeping accents * Fix tokenization test * Fix FNet Tokenizer Fast * fix tests * make style * Add tips to FNet docs Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
393 lines
20 KiB
Python
393 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2019 Hugging Face inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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from transformers import FNetTokenizer, FNetTokenizerFast
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
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from .test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/spiece.model")
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@require_sentencepiece
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@require_tokenizers
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class FNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = FNetTokenizer
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rust_tokenizer_class = FNetTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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test_sentencepiece_ignore_case = True
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = FNetTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "this is a test"
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output_text = "this is a test"
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return input_text, output_text
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "<pad>"
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token_id = 0
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<pad>")
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self.assertEqual(vocab_keys[1], "<unk>")
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self.assertEqual(vocab_keys[-1], "▁eloquent")
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self.assertEqual(len(vocab_keys), 30_000)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 30_000)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_full_tokenizer(self):
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tokenizer = FNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁", "T", "his", "▁is", "▁a", "▁test"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [13, 1, 4398, 25, 21, 1289])
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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"▁",
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"<unk>",
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"▁was",
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"▁born",
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"▁in",
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"▁9",
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"2000",
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",",
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"▁and",
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"▁this",
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"▁is",
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"▁fal",
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"s",
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"<unk>",
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".",
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],
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)
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def test_sequence_builders(self):
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tokenizer = FNetTokenizer(SAMPLE_VOCAB)
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
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assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [
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tokenizer.sep_token_id
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]
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# Overriden Tests
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def test_padding(self, max_length=50):
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if not self.test_slow_tokenizer:
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# as we don't have a slow version, we can't compare the outputs between slow and fast versions
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return
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
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pad_token_id = tokenizer_p.pad_token_id
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# Encode - Simple input
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input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
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input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
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self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
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input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
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self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.encode("This is a simple input", padding="longest")
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input_p = tokenizer_p.encode("This is a simple input", padding=True)
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self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
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# Encode - Pair input
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input_r = tokenizer_r.encode(
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"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
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)
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input_p = tokenizer_p.encode(
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"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
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)
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self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.encode(
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"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
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)
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input_p = tokenizer_p.encode(
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"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
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)
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self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
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input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
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self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
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# Encode_plus - Simple input
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input_r = tokenizer_r.encode_plus(
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"This is a simple input", max_length=max_length, pad_to_max_length=True
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)
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input_p = tokenizer_p.encode_plus(
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"This is a simple input", max_length=max_length, pad_to_max_length=True
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)
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self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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input_r = tokenizer_r.encode_plus(
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"This is a simple input", max_length=max_length, padding="max_length"
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)
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input_p = tokenizer_p.encode_plus(
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"This is a simple input", max_length=max_length, padding="max_length"
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)
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self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
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input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
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self.assert_padded_input_match(
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input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
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)
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# Encode_plus - Pair input
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input_r = tokenizer_r.encode_plus(
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"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
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)
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input_p = tokenizer_p.encode_plus(
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"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
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)
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self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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input_r = tokenizer_r.encode_plus(
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"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
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)
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input_p = tokenizer_p.encode_plus(
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"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
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)
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self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
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input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
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self.assert_padded_input_match(
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input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
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)
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# Batch_encode_plus - Simple input
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input_r = tokenizer_r.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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pad_to_max_length=True,
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)
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input_p = tokenizer_p.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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pad_to_max_length=True,
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)
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self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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padding="max_length",
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)
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input_p = tokenizer_p.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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padding="max_length",
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)
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self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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padding="longest",
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)
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input_p = tokenizer_p.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"],
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max_length=max_length,
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padding=True,
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)
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self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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input_r = tokenizer_r.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"], padding="longest"
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)
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input_p = tokenizer_p.batch_encode_plus(
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["This is a simple input 1", "This is a simple input 2"], padding=True
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)
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self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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# Batch_encode_plus - Pair input
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input_r = tokenizer_r.batch_encode_plus(
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[
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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],
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max_length=max_length,
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truncation=True,
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padding="max_length",
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)
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input_p = tokenizer_p.batch_encode_plus(
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[
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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],
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max_length=max_length,
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truncation=True,
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padding="max_length",
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)
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self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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input_r = tokenizer_r.batch_encode_plus(
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[
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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],
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padding=True,
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)
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input_p = tokenizer_p.batch_encode_plus(
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[
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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],
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padding="longest",
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)
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self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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# Using pad on single examples after tokenization
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input_r = tokenizer_r.encode_plus("This is a input 1")
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input_r = tokenizer_r.pad(input_r)
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input_p = tokenizer_r.encode_plus("This is a input 1")
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input_p = tokenizer_r.pad(input_p)
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self.assert_padded_input_match(
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input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
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)
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# Using pad on single examples after tokenization
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input_r = tokenizer_r.encode_plus("This is a input 1")
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input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
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input_p = tokenizer_r.encode_plus("This is a input 1")
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input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
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self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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# Using pad after tokenization
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input_r = tokenizer_r.batch_encode_plus(
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["This is a input 1", "This is a much longer input whilch should be padded"]
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)
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input_r = tokenizer_r.pad(input_r)
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input_p = tokenizer_r.batch_encode_plus(
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["This is a input 1", "This is a much longer input whilch should be padded"]
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)
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input_p = tokenizer_r.pad(input_p)
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self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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# Using pad after tokenization
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input_r = tokenizer_r.batch_encode_plus(
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["This is a input 1", "This is a much longer input whilch should be padded"]
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)
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input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
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input_p = tokenizer_r.batch_encode_plus(
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["This is a input 1", "This is a much longer input whilch should be padded"]
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)
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input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
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self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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def assert_batch_padded_input_match(
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self,
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input_r: dict,
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input_p: dict,
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max_length: int,
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pad_token_id: int,
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model_main_input_name: str = "input_ids",
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):
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for i_r in input_r.values():
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self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
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len(i_r[1]), max_length
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)
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self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
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len(i_r[1]), max_length
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)
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for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
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self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[4, 4616, 107, 163, 328, 14, 63, 1726, 106, 11954, 16659, 23, 83, 16688, 11427, 328, 107, 36, 11954, 16659, 23, 83, 16688, 6153, 82, 961, 16688, 3474, 16710, 1696, 2306, 16688, 10854, 2524, 3827, 561, 163, 3474, 16680, 62, 226, 2092, 16680, 379, 3474, 16660, 16680, 2436, 16667, 16671, 16680, 999, 87, 3474, 16680, 2436, 16667, 5208, 800, 16710, 68, 2018, 2959, 3037, 163, 16663, 11617, 16710, 36, 2018, 2959, 4737, 163, 16663, 16667, 16674, 16710, 91, 372, 5087, 16745, 2205, 82, 961, 3608, 38, 1770, 16745, 7984, 36, 2565, 751, 9017, 1204, 864, 218, 1244, 16680, 11954, 16659, 23, 83, 36, 14686, 23, 7619, 16678, 5], [4, 28, 532, 65, 1929, 33, 391, 16688, 3979, 9, 2565, 7849, 299, 225, 34, 2040, 305, 167, 289, 16667, 16078, 32, 1966, 181, 4626, 63, 10575, 71, 851, 1491, 36, 624, 4757, 38, 208, 8038, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 13, 1467, 5187, 26, 2521, 4567, 16664, 372, 13, 16209, 3314, 16678, 5, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="google/fnet-base",
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revision="58e0d1f96af163dc8d0a84a2fddf4bd403e4e802",
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)
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